A window based method to reduce the end-effect in Empirical Mode Decomposition

Empirical Mode Decomposition technique (EMD) is a recent development in non-stationary and non-linear data analysis. It is an algorithm which adaptively decomposes the signal in the sum of Intrinsic Mode Functions (IMFs) from which the instantaneous frequency can be easily computed. EMD has proven its effectiveness but is still affected from various problems. One of these is the “end-effect”, a phenomenon occurring at the start and at the end of the data due to the splines fitting on which the EMD is based. Various techniques have been tried to overcome the end-effect, like different data extension or mirroring procedures at the data boundary. In this paper we made use of the IMFs orthogonality property to apply a symmetrical window to the data before EMD for end-effect reduction. Subsequently the IMFs are post-processed to compensate for data alteration due to windowing. The simulations show that IMFs obtained with this method are of better quality near the data boundaries while remaining almost identical to classical EMD ones.